## Problem The agent builder (LLM) misinterprets the HumanInTheLoop block outputs. It thinks `approved_data` and `rejected_data` will yield status strings like "APPROVED" or "REJECTED" instead of understanding that the actual input data passes through. This leads to unnecessary complexity - the agent builder adds comparison blocks to check for status strings that don't exist. ## Solution Enriched the block docstring and all input/output field descriptions to make it explicit that: 1. The output is the actual data itself, not a status string 2. The routing is determined by which output pin fires 3. How to use the block correctly (connect downstream blocks to appropriate output pins) ## Changes - Updated block docstring with clear "How it works" and "Example usage" sections - Enhanced `data` input description to explain data flow - Enhanced `name` input description for reviewer context - Enhanced `approved_data` output to explicitly state it's NOT a status string - Enhanced `rejected_data` output to explicitly state it's NOT a status string - Enhanced `review_message` output for clarity ## Testing Documentation-only change to schema descriptions. No functional changes. Fixes SECRT-1930 <!-- greptile_comment --> <h2>Greptile Overview</h2> <details><summary><h3>Greptile Summary</h3></summary> Enhanced documentation for the `HumanInTheLoopBlock` to clarify how output pins work. The key improvement explicitly states that output pins (`approved_data` and `rejected_data`) yield the actual input data, not status strings like "APPROVED" or "REJECTED". This prevents the agent builder (LLM) from misinterpreting the block's behavior and adding unnecessary comparison blocks. **Key changes:** - Added "How it works" and "Example usage" sections to the block docstring - Clarified that routing is determined by which output pin fires, not by comparing output values - Enhanced all input/output field descriptions with explicit data flow explanations - Emphasized that downstream blocks should be connected to the appropriate output pin based on desired workflow path This is a documentation-only change with no functional modifications to the code logic. </details> <details><summary><h3>Confidence Score: 5/5</h3></summary> - This PR is safe to merge with no risk - Documentation-only change that accurately reflects the existing code behavior. No functional changes, no runtime impact, and the enhanced descriptions correctly explain how the block outputs work based on verification of the implementation code. - No files require special attention </details> <!-- greptile_other_comments_section --> <!-- /greptile_comment --> Co-authored-by: Zamil Majdy <zamil.majdy@agpt.co>
AutoGPT Platform
Welcome to the AutoGPT Platform - a powerful system for creating and running AI agents to solve business problems. This platform enables you to harness the power of artificial intelligence to automate tasks, analyze data, and generate insights for your organization.
Getting Started
Prerequisites
- Docker
- Docker Compose V2 (comes with Docker Desktop, or can be installed separately)
Running the System
To run the AutoGPT Platform, follow these steps:
-
Clone this repository to your local machine and navigate to the
autogpt_platformdirectory within the repository:git clone <https://github.com/Significant-Gravitas/AutoGPT.git | git@github.com:Significant-Gravitas/AutoGPT.git> cd AutoGPT/autogpt_platform -
Run the following command:
cp .env.default .envThis command will copy the
.env.defaultfile to.env. You can modify the.envfile to add your own environment variables. -
Run the following command:
docker compose up -dThis command will start all the necessary backend services defined in the
docker-compose.ymlfile in detached mode. -
After all the services are in ready state, open your browser and navigate to
http://localhost:3000to access the AutoGPT Platform frontend.
Running Just Core services
You can now run the following to enable just the core services.
# For help
make help
# Run just Supabase + Redis + RabbitMQ
make start-core
# Stop core services
make stop-core
# View logs from core services
make logs-core
# Run formatting and linting for backend and frontend
make format
# Run migrations for backend database
make migrate
# Run backend server
make run-backend
# Run frontend development server
make run-frontend
Docker Compose Commands
Here are some useful Docker Compose commands for managing your AutoGPT Platform:
docker compose up -d: Start the services in detached mode.docker compose stop: Stop the running services without removing them.docker compose rm: Remove stopped service containers.docker compose build: Build or rebuild services.docker compose down: Stop and remove containers, networks, and volumes.docker compose watch: Watch for changes in your services and automatically update them.
Sample Scenarios
Here are some common scenarios where you might use multiple Docker Compose commands:
-
Updating and restarting a specific service:
docker compose build api_srv docker compose up -d --no-deps api_srvThis rebuilds the
api_srvservice and restarts it without affecting other services. -
Viewing logs for troubleshooting:
docker compose logs -f api_srv ws_srvThis shows and follows the logs for both
api_srvandws_srvservices. -
Scaling a service for increased load:
docker compose up -d --scale executor=3This scales the
executorservice to 3 instances to handle increased load. -
Stopping the entire system for maintenance:
docker compose stop docker compose rm -f docker compose pull docker compose up -dThis stops all services, removes containers, pulls the latest images, and restarts the system.
-
Developing with live updates:
docker compose watchThis watches for changes in your code and automatically updates the relevant services.
-
Checking the status of services:
docker compose psThis shows the current status of all services defined in your docker-compose.yml file.
These scenarios demonstrate how to use Docker Compose commands in combination to manage your AutoGPT Platform effectively.
Persisting Data
To persist data for PostgreSQL and Redis, you can modify the docker-compose.yml file to add volumes. Here's how:
-
Open the
docker-compose.ymlfile in a text editor. -
Add volume configurations for PostgreSQL and Redis services:
services: postgres: # ... other configurations ... volumes: - postgres_data:/var/lib/postgresql/data redis: # ... other configurations ... volumes: - redis_data:/data volumes: postgres_data: redis_data: -
Save the file and run
docker compose up -dto apply the changes.
This configuration will create named volumes for PostgreSQL and Redis, ensuring that your data persists across container restarts.
API Client Generation
The platform includes scripts for generating and managing the API client:
pnpm fetch:openapi: Fetches the OpenAPI specification from the backend service (requires backend to be running on port 8006)pnpm generate:api-client: Generates the TypeScript API client from the OpenAPI specification using Orvalpnpm generate:api: Runs both fetch and generate commands in sequence
Manual API Client Updates
If you need to update the API client after making changes to the backend API:
-
Ensure the backend services are running:
docker compose up -d -
Generate the updated API client:
pnpm generate:api
This will fetch the latest OpenAPI specification and regenerate the TypeScript client code.